Deep Adversarial Reinforcement Learning Method to Generate Control Policies Robust Against Worst-Case Value Predictions

Deep Adversarial Reinforcement Learning Method to Generate Control Policies Robust Against Worst-Case Value Predictions

Over the last decade, methods for autonomous control by artificial intelligence have been extensively developed based on deep reinforcement learning (DRL) technologies. However, despite these advances, robustness to noise in observation data remains as an issue in autonomous control policies impleme...

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Bibliographic Details
Main Authors: Kohei Ohashi, Kosuke Nakanishi, Yuji Yasui, Shin Ishii
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
adversarial example
robustness
regularization
Online Access:https://ieeexplore.ieee.org/document/10250423/

Internet

https://ieeexplore.ieee.org/document/10250423/

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